ISIC-2019 / train_vit.py
XXXXRT666
1
1de27a5
from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
from transformers import ViTForImageClassification, ViTImageProcessor, Trainer, TrainingArguments, ViTConfig
from PIL import Image
from torch.optim import AdamW
from torch.optim.lr_scheduler import StepLR
import torch
import numpy as np
from sklearn.metrics import f1_score, accuracy_score, recall_score, precision_score
import os
MODEL_NAME = "google/vit-base-patch16-224"
SIZE = "base"
PATCH = 16
IMAGE_SIZE = 224
BATCH_SIZE = 128
OPTIMIZER = "AdamW"
SCHEDULER = "StepLR"
NAME = 'no-pretrain'
IMAGE_PATH = '/root/autodl-tmp/ISIC-2019'
TRAIN_CSV_PATH = '/root/autodl-tmp/ISIC-2019/train_labels.csv'
TEST_CSV_PATH = '/root/autodl-tmp/ISIC-2019/test_labels.csv'
checkpoint_dir = "/root/autodl-tmp/ISIC-2019/logs/vit-base-patch16-224-bs128-AdamW-StepLR-lables-9"
if os.path.isdir(checkpoint_dir) and any(os.scandir(checkpoint_dir)):
checkpoint = max([os.path.join(checkpoint_dir, d) for d in os.listdir(checkpoint_dir) if "checkpoint" in d], key=os.path.getctime)
else:
checkpoint = None
checkpoint = "/root/autodl-tmp/ISIC-2019/logs/vit-base-patch16-224-bs128-AdamW-StepLR-lables-9-train-linear/checkpoint-895"
checkpoint = None
print(f"从检查点 {checkpoint} 恢复训练")
processed_dataset_path = f"/root/autodl-tmp/ISIC-2019/dataset-{IMAGE_SIZE}"
# processed_dataset_path = f"/root/autodl-tmp/dataset-{IMAGE_SIZE}"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# device = torch.device("mps")
processor = ViTImageProcessor.from_pretrained(MODEL_NAME)
def preprocess_image_train(example):
image = Image.open(os.path.join(IMAGE_PATH,'train', example["image"])).convert("RGB")
example["pixel_values"] = processor(images=image, return_tensors="pt")["pixel_values"].squeeze(0).numpy()
labels = example.copy()
example["labels"] = np.array([labels["MEL"], labels["NV"], labels["BCC"], labels["AK"], labels["BKL"], labels["DF"], labels["VASC"], labels["SCC"], labels["UNK"]], dtype=np.float32)
return example
def preprocess_image_test(example):
image = Image.open(os.path.join(IMAGE_PATH,'test', example["image"])).convert("RGB")
example["pixel_values"] = processor(images=image, return_tensors="pt")["pixel_values"].squeeze(0).numpy()
labels = example.copy()
example["labels"] = np.array([labels["MEL"], labels["NV"], labels["BCC"], labels["AK"], labels["BKL"], labels["DF"], labels["VASC"], labels["SCC"], labels["UNK"]], dtype=np.float32)
return example
if os.path.exists(processed_dataset_path):
dataset = load_from_disk(processed_dataset_path,keep_in_memory=True)
print("LOADED")
else:
train_dataset = load_dataset('csv', data_files=TRAIN_CSV_PATH, keep_in_memory=True)["train"]
test_dataset = load_dataset('csv', data_files=TEST_CSV_PATH, keep_in_memory=True)["train"]
train_dataset = train_dataset.map(preprocess_image_train, batched=False, num_proc=2)
test_dataset = test_dataset.map(preprocess_image_test, batched=False, num_proc=2)
dataset = DatasetDict({
'train': train_dataset,
'test': test_dataset
})
dataset.save_to_disk(processed_dataset_path,num_proc=2)
print(f"SAVED TO {processed_dataset_path}")
train_dataset = dataset['train']
test_dataset = dataset['test']
num_labels = 9
# model = ViTForImageClassification.from_pretrained(
# MODEL_NAME,
# num_labels=num_labels,
# problem_type="multi_label_classification",
# ignore_mismatched_sizes=True
# ).to(device)
# for param in model.parameters():
# param.requires_grad = False
# for param in model.classifier.parameters():
# param.requires_grad = True
config = ViTConfig(image_size=IMAGE_SIZE, num_labels=num_labels, problem_type="multi_label_classification", patch_size = PATCH)
model = ViTForImageClassification(config)
training_args = TrainingArguments(
output_dir=f"/root/autodl-tmp/ISIC-2019/logs/vit-{SIZE}-patch{PATCH}-{IMAGE_SIZE}-bs{BATCH_SIZE}-{OPTIMIZER}-{SCHEDULER}-lables-{num_labels}-{NAME}",
evaluation_strategy="epoch",
learning_rate=5e-5,
per_device_train_batch_size=BATCH_SIZE,
per_device_eval_batch_size=BATCH_SIZE,
num_train_epochs=20,
save_strategy="epoch",
logging_dir=f"/root/autodl-tmp/ISIC-2019/logs/vit-{SIZE}-patch{PATCH}-{IMAGE_SIZE}-bs{BATCH_SIZE}-{OPTIMIZER}-{SCHEDULER}-lables-{num_labels}-{NAME}/logs",
logging_steps=10,
report_to="tensorboard",
fp16=True,
dataloader_num_workers = 8
)
def compute_metrics(pred):
logits, labels = pred
predictions = (logits >= 0.5).astype(int)
f1 = f1_score(labels, predictions, average="macro")
accuracy = accuracy_score(labels, predictions)
recall = recall_score(labels, predictions, average="macro")
precision = precision_score(labels, predictions, average="macro")
return {
"accuracy": accuracy,
"f1": f1,
"recall": recall,
"precision": precision,
}
learning_rate = 5e-5
weight_decay = 0.01
step_size = 1000
gamma = 0.1
optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# scheduler = StepLR(optimizer, step_size=step_size, gamma=gamma)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=test_dataset,
compute_metrics=compute_metrics,
optimizers=(optimizer,None)
# optimizers=(optimizer, scheduler)
)
# trainer.train()
trainer.train(resume_from_checkpoint=checkpoint)